We consider three sequence generation tasks in which sequences are generated left to right, with each action appending one symbol from a vocabulary to a partial sequence: a synthetic task with varying sequence lengths and vocabulary sizes (§REF ), a practical biological sequence design task (§REF ), and a new protein design task with longer sequences (REF ). For all three tasks, we consider the baselines Soft Actor-Critic [1]}, [2]}, A2C with Entropy regularization [3]}, [4]} and MARS-like MCMC [5]} and compare them with three GFlowNet training objectives: TB, FM, and SubTB(\(\lambda \) ).
